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We developed and applied a model-driven feedforward control approach to mitigate thermal-induced flaw formation in laser powder bed fusion (LPBF) additive manufacturing process. The key idea was to avert heat buildup in a LPBF part before it is printed by adapting process parameters layer-by-layer based on insights from a physics-based thermal simulation model. The motivation being to replace cumbersome empirical build-and-test parameter optimization with a physics-guided strategy. The approach consisted of three steps: prediction, analysis, and correction. First, the temperature distribution of a part was predicted rapidly using a graph theory-based computational thermal model. Second, the model-derived thermal trends were analyzed to isolate layers of potential heat buildup. Third, heat buildup in affected layers was corrected before printing by adjusting process parameters optimized through iterative simulations. The effectiveness of the approach was demonstrated experimentally on two separate build plates. In the first build plate, termed fxed processing, ten different nickel alloy 718 parts were produced under constant processing conditions. On a second identical build plate, called con-trolled processing, the laser power and dwell time for each part was adjusted before printing based on thermal simulations to avoid heat buildup. To validate the thermal model predictions, the surface tem-perature of each part was tracked with a calibrated infrared thermal camera. Post-process the parts were examined with non-destructive and destructive materials characterization techniques. Compared to fixed processing, parts produced under controlled processing showed superior geometric accuracy and resolu-tion, finer grain size, increased microhardness, and reduced surface roughness.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). National Science Foundation (NSF); Department of Energy (DoE) [OIA-1929172, CMMI-1920245, CMMI-1739696, ECCS-2020246, PFI-TT 2044710, CMMI-1752069, CMMI-1719388, DE-SC0021136]; NSF INTERN program [CMMI-1752069]; CMMI Data Science Activities [CMMI-1752069]; Major Research instrumentation grant [CMMI-1920245]; DoE [DE-SC0021136]; National Nanotechnology Coordinated Infrastructure [ECCS: 2025298]; Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience; Nanoengineering Research Core Facility at the University of Nebraska Lincoln; NSF Research Experience for Teachers grant [RET: EEC1953382] Published version This work was supported by the National Science Foundation (NSF) and Department of Energy (DoE) under awards OIA-1929172, CMMI-1920245, CMMI-1739696, ECCS-2020246, PFI-TT 2044710,CMMI-1752069, CMMI-1719388, and DE-SC0021136. Understanding the causal influence of process parameters and thermal history on part quality and detection of defect formation using in-situsensing was the major aspect of CMMI-1752069 (Program Officer:Kevin Chou). The use of graph theory for feedforward control in additive manufacturing was proposed in OIA-1929172 (Program Officer: Jose Colom-Ustariz). The experiments for this work were carried out at Edison Welding Institute (EWI) by Alex Riensche , Reza Yavari, and Ben Bevans through OIA-1929172 with guidance from Ajay Krishnan. Supplemental funding for CMMI-1752069 was obtained through the NSF INTERN program (Program Officer: Prakash Balan) and CMMI Data Science Activities (Program Officer: Martha Dodson). These supplements funded Reza Yavari's andZiyad Smoqi's research. Commercialization of the graph theory thermal approach for ultrafast simulation of metal additive manufacturing processes is being pursued under PFI-TT (Program Officer: Samir Iqbal).The X-ray CT analysis was conducted on the instrument partially funded through the Major Research instrumentation grant(CMMI-1920245, program officer: Wendy C. Crone). Ben Bevans'work was funded partially through the DoE Grant DE-SC0021136.The materials characterization research was performed in part in the Nebraska Nanoscale Facility: National Nanotechnology Coordinated Infrastructure under award no. ECCS: 2025298, and with support from the Nebraska Research Initiative through the Nebraska Center for Materials and Nanoscience and the Nanoengineering Research Core Facility at the University of Nebraska Lincoln. Josie Gilligan was supported through NSF Research Experience for Teachers grant RET: EEC1953382.All builds and data collection efforts were executed at EWI, and Ajay Krishnan's efforts was sponsored by the EWI Additive Manu-facturing. The authors thank Dr. Brandon Lane of National Institute of Standards and Technology, and Dr. Abdalla Nassar formerly of Penn State, Applied research Labs for valuable insights and discussions. |
